Computer Vision Approach to Morphometric Feature Analysis of Basal Cell Nuclei for Evaluating Malignant Potentiality of Oral Submucous Fibrosis

• Published in: Journal of medical systems 2012-06, Vol.36 (3), p.1745-1756
• Main Authors: Muthu Rama Krishnan, M., Pal, Mousumi, Paul, Ranjan Rashmi, Chakraborty, Chandan, Chatterjee, Jyotirmoy, Ray, Ajoy K.
• Format: Article
• Language: English
• Subjects: Algorithms; Bayes Theorem; Biological; Cell Nucleus - pathology; Cellular; Epithelium; Fibrosis; Health Informatics; Health Sciences; Humans; Image Processing, Computer-Assisted; Imaging, Three-Dimensional; India; Information processing; Information systems; Medical disorders; Medicine; Medicine & Public Health; Morphology; Mouth; Neoplasms, Basal Cell - pathology; Oral cancer; Oral Submucous Fibrosis - diagnosis; Oral Submucous Fibrosis - physiopathology; Original Paper; Pattern analysis; Staining; Statistics for Life Sciences; Surface chemistry; Three dimensional
• ISSN: 0148-5598; 1573-689X
• DOI: 10.1007/s10916-010-9634-5

This research work presents a quantitative approach for analysis of histomorphometric features of the basal cell nuclei in respect to their size, shape and intensity of staining, from surface epithelium of Oral Submucous Fibrosis showing dysplasia (OSFD) to that of the Normal Oral Mucosa (NOM). For all biological activity, the basal cells of the surface epithelium form the proliferative compartment and therefore their morphometric changes will spell the intricate biological behavior pertaining to normal cellular functions as well as in premalignant and malignant status. In view of this, the changes in shape, size and intensity of staining of the nuclei in the basal cell layer of the NOM and OSFD have been studied. Geometric, Zernike moments and Fourier descriptor (FD) based as well as intensity based features are extracted for histomorphometric pattern analysis of the nuclei. All these features are statistically analyzed along with 3D visualization in order to discriminate the groups. Results showed increase in the dimensions (area and perimeter), shape parameters and decreasing mean nuclei intensity of the nuclei in OSFD in respect to NOM. Further, the selected features are fed to the Bayesian classifier to discriminate normal and OSFD. The morphometric and intensity features provide a good sensitivity of 100%, specificity of 98.53% and positive predicative accuracy of 97.35%. This comparative quantitative characterization of basal cell nuclei will be of immense help for oral onco-pathologists, researchers and clinicians to assess the biological behavior of OSFD, specially relating to their premalignant and malignant potentiality. As a future direction more extensive study involving more number of disease subjects is observed.